This paper develops a network to analyze inter-industry risk spillovers during high and low volatility periods. Our findings indicate that China's Industrials and Consumer Discretionary exhibit the greatest levels of spillovers in both high and low volatility states. Notably, our results demonstrate the "event-driven" character of structural changes to the network during periods of pronounced risk events. At the same time, the economic and financial network exhibits clear "small world" characteristics. Additionally, in the high volatility stage, the inter-industry risk contagion network becomes more complex, featuring greater connectivity and direct contagion paths. Furthermore, concerning the spillover connection between finance and the real sector, the real economy serves as a net exporter of risk. The study's findings can assist government agencies in preventing risk contagion between the financial market and the real economy. The empirical evidence and policy lessons provide valuable insights for effective risk management.

Based on the formation and evolution of systemic risk, this study proposes high and low volatility spillover networks and explores the characteristics of the evolution of systemic risk in Chinese financial market, and identifies the source of risk accumulation and risk outbreak, as well as the corresponding contagion mechanisms. Moreover, a new multiscale decomposition method (MVMD) is used to decompose high and low volatility into different time frequency components (short-term and long-term), and the corresponding network is constructed. Upon comparing topological characteristics on each layer from system and individual levels, our results reveal that high and low volatility spillover networks have different network characteristics and evolution behaviors. At the individual level, bond market is always the largest risk net-receivers in the high and low volatility networks, while the futures market and the currency market are respectively risk net-emitters in the high and low volatility networks. Additionally, compared with high volatility network, the low volatility network has greater predictive ability for financial risk. Finally, frequency analysis demonstrates that high-low volatility networks have different spillover intensity and network structure at different time frequencies. The above findings are beneficial for policy makers and investors to formulate appropriate strategies in different evolution of systemic risk and time frequency.